test: update test

ECALC-1361

src/libecalc/core/models/turbine.py

@@ -36,16 +36,15 @@ def __init__(
     def max_power(self) -> Optional[float]:
         # Max power the compressor can ask for, based on max turbine load
         # Note: the scaling is opposite to the turbine load adjustment
-        # Why a*x - b and not a*x + b?
         return (
             self._maximum_load * self.data_transfer_object.energy_usage_adjustment_factor
             - self.data_transfer_object.energy_usage_adjustment_constant
         )
 
     def evaluate(self, load: NDArray[np.float64], fuel_lower_heating_value: float = 0) -> TurbineResult:
         # Calibration of turbine load:
-        # Why not linear transformation? To ensure positive adjustment factor gives loss in turbine?
-        # If a*x + b: positive a will give gain in turbine (better power2load ratio)
+        # Linear adjustment a´*x + b´, but where a´=1/a and b´=b/a.
+        # This is probably to ensu
         load_adjusted = np.where(
             load > 0,
             (load + self.data_transfer_object.energy_usage_adjustment_constant)

src/tests/libecalc/core/models/test_turbine.py

@@ -64,14 +64,12 @@ def test_turbine_with_power_adjustment_constant_and_factor(turbine: TurbineModel
     # Result with adjustment:
     result_adjusted = turbine.evaluate(load=np.asarray([load]))
 
-    power_to_energy_ratio = result.power[0] / result.energy_usage[0]
-    power_to_energy_ratio_adjusted = result_adjusted.power[0] / result_adjusted.energy_usage[0]
-
     # Verify that adjustment is correct:
     np.testing.assert_allclose(
         (np.asarray(result.load) + energy_usage_adjustment_constant) / energy_usage_adjustment_factor,
         result_adjusted.load,
     )
 
-    # Verify that turbine uses more energy to deliver same amount of mechanical power
-    assert power_to_energy_ratio_adjusted < power_to_energy_ratio
+    # Verify that turbine efficiency is reduced with increased adjustment factor, i.e. higher load (and low constant):
+    # Efficiency is increasing with load (input data)
+    assert result_adjusted.efficiency < result.efficiency